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Automatic evaluation of text generation tasks (e.g.
compare-mt: A tool for holistic comparison of language generation systems
Graham Neubig, Zi-Yi Dou, Junjie Hu, Paul Michel, Danish Pruthi, and Xinyi Wang. 2019 · 1903
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Bertscore: Evaluating text generation with bert
Tianyi Zhang, Varsha Kishore, Felix Wu, Kilian Q Weinberger, and Yoav Artzi. 2019 · 1909
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Automatic evaluation of machine translation quality using n-gram co-occurrence statistics
George Doddington. 2002 · 2002
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Bleu: a method for automatic evaluation of machine translation
Kishore Papineni, Salim Roukos, Todd Ward, and Wei-Jing Zhu. 2002 · 2002
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Rouge: A package for automatic evaluation of summaries
Chin-Yew Lin. 2004 · 2004
Earlier work this paper cites.
Meteor: An automatic metric for mt evaluation with improved correlation with human judgments
Satanjeev Banerjee and Alon Lavie. 2005 · 2005
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A study of translation edit rate with targeted human annotation
Matthew Snover, Bonnie Dorr, Richard Schwartz, Linnea Micciulla, and John Makhoul. 2006 · 2006
Earlier work this paper cites.
Automatic evaluation of translation quality for distant language pairs
Hideki Isozaki, Tsutomu Hirao, Kevin Duh, Katsuhito Sudoh, and Hajime Tsukada. 2010 · 2010
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ibleu: Interactively debugging and scoring statistical machine translation systems
Nitin Madnani. 2011 · 2011
Cited alongside, same era.
A thousand frames in just a few words: Lingual description of videos through latent topics and sparse object stitching
Pradipto Das, Chenliang Xu, Richard F Doell, and Jason J Corso. 2013 · 2013
Cited alongside, same era.
Microsoft coco: Common objects in context
Tsung-Yi Lin, Michael Maire, Serge Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dollár, and C Lawrence Zitnick. 2014 · 2014
Cited alongside, same era.
Mt-compareval: Graphical evaluation interface for machine translation development
Ondřej Klejch, Eleftherios Avramidis, Aljoscha Burchardt, and Martin Popel. 2015 · 2015
Cited alongside, same era.
chrf: character n-gram f-score for automatic mt evaluation
Maja Popović. 2015 · 2015
Cited alongside, same era.
Cider: Consensus-based image description evaluation
Automatic differentiation in PyTorch
Adam Paszke, Sam Gross, Soumith Chintala, Gregory Chanan, Edward Yang, Zachary DeVito, Zeming Lin, Alban Desmaison, Luca Antiga, and Adam Lerer. 2017 · 2017
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Shikhar Sharma, Layla El Asri, Hannes Schulz, and Jeremie Zumer. 2017 · 2017
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Massively multilingual sentence embeddings for zero-shot cross-lingual transfer and beyond
Mikel Artetxe and Holger Schwenk. 2018 · 2018
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Opennmt: Neural machine translation toolkit
Guillaume Klein, Yoon Kim, Yuntian Deng, Vincent Nguyen, Jean Senellart, and Alexander M. Rush. 2018 · 2018
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Vis-eval metric viewer: A visualisation tool for inspecting and evaluating metric scores of machine translation output
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Ramakrishna Vedantam, C Lawrence Zitnick, and Devi Parikh. 2015 · 2015
Cited alongside, same era.
Visual tools for debugging neural language models
Xin Rong and Eytan Adar. 2016 · 2016
Cited alongside, same era.
Google’s neural machine translation system: Bridging the gap between human and machine translation
Yonghui Wu, Mike Schuster, Zhifeng Chen, Quoc V Le, Mohammad Norouzi, Wolfgang Macherey, Maxim Krikun, Yuan Cao, Qin Gao, Klaus Macherey, et al. 2016 · 2016
Cited alongside, same era.
David Steele and Lucia Specia. 2018 · 2018
Later among the works it cites.
When and why are pre-trained word embeddings useful for neural machine translation
Qi Ye, Sachan Devendra, Felix Matthieu, Padmanabhan Sarguna, and Neubig Graham. 2018 · 2018
Later among the works it cites.
Seq2seq-vis: A visual debugging tool for sequence-to-sequence models
Hendrik Strobelt, Sebastian Gehrmann, Michael Behrisch, Adam Perer, Hanspeter Pfister, and Alexander M Rush. 2019 · 2019
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